Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2018

Abstract

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public transportation system. In this paper, we analyse the public transportation patterns in a densely populated city, Chicago, USA, using comprehensive datasets covering the transportation records on shared bikes, buses, taxis and subways collected over one year's time. Specifically, we apply self-regulated clustering methods to reveal both the majority transportation patterns and the irregular ones. Other than reporting the autonomously discovered transportation patterns, we also show that our method achieves better clustering performance than the benchmarking methods.

Keywords

Self-regulated clustering, shared bike, densely populated city, public transportation pattern

Discipline

Databases and Information Systems | Software Engineering | Transportation

Research Areas

Data Science and Engineering

Publication

Proceedings of the 3rd International Conference on Crowd Science and Engineering: ICCSE'18, Singapore, 2018 July 28-31

First Page

1

Last Page

8

ISBN

9781450365871

Identifier

10.1145/3265689.3265697

Publisher

ACM

City or Country

Singapore

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